Intrusion Detection Using Tree Based Classifiers

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2020 by IJCTT Journal
Volume-68 Issue-2
Year of Publication : 2020
Authors : Ashalata Panigrahi, Manas Ranjan Patra
DOI :  10.14445/22312803/IJCTT-V68I2P109

MLA

MLA Style:Ashalata Panigrahi, Manas Ranjan Patra "Intrusion Detection Using Tree Based Classifiers" International Journal of Computer Trends and Technology 68.2 (2020):59-63.

APA Style: Ashalata Panigrahi, Manas Ranjan Patra (2020). Intrusion Detection Using Tree Based Classifiers  International Journal of Computer Trends and Technology, 68(2),59-63.

Abstract
Growing cyber-crimes have become a serious concern for network users. It has become a real challenge for organizations to develop network security systems to protect data from all kinds of illegal access. Since intruders keep applying different techniques to break the security barriers, the techniques to counter such attacks are also being developed by the researchers. In this work, a model has been proposed for building an effective intrusion detection system using tree based classification techniques, namely, BF Tree, FT, J48, NB Tree, Random Forest, and Random Tree. Further, three nature-inspired and two heuristic search based methods have been applied for selecting important features prior to the classification process. The performance of the model has been evaluated on the NSL-KDD dataset in terms of accuracy, precision, detection rate, and false alarm rate.

Reference
[1] O. Joldzic, Z. Djuric, and P. Vuletic, ?A transparent and scalable anomaly-based dos detection method,? Computer Networks, vol. 104, pp. 27–42, 2016.
[2] F. Salo, A. B. Nassif, A. Essex, ?Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection? , Computer Network, pp. 164–175, 2018.
[3] H. Hota and A. K. Shrivas, ?Decision tree techniques applied on NSL-KDD data and its comparison with various feature selection techniques ? , Advanced Computing, Networking and Informatics vol.1. Springer, pp. 205–211, 2014.
[4] W. L. Al-Yaseen, Z. A. Othman, and M. Z. A. Nazri, ?Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system? , Expert Systems with Applications., vol. 67, pp. 296–303, 2017.
[5] A. Akyol, M. Hacibeyoglu, and B. Karlik, ?Design of multilevel hybrid classifier with variant feature sets for intrusion detection system. IEICE Trans. Inf. Syst, pp.1810–1821, 2016.
[6] Ahmim, M. Derdour, and M. A. Ferrag, ? An intrusion detection system based on combining probability predictions of a tree of classifiers? , International Journal of Communication System, vol. 31, pp.1–14, 2018.
[7] L. J. Li, Y. Yu, S. S. Bai, J. J. Cheng, and X.Y. Chen? , Towards effective network intrusion detection: A hybrid model integrating Gini index and GBDT with PSO? , Journal of Sensors, vol. 6, pp.1–9, 2018.
[8] S. Shamshirband, B. Daghighi, N.B. Anuar, M.L.M. Kiah, A. Patel, A. Abraham, ? Co-FQL: Anomaly detection using cooperative fuzzy Q-learning in network?, Journal of Intelligent and Fuzzy System, vol. 28, pp.1345–1357, 2015.
[9] S. Haijian, Best-First Decision Tree Learning. Masters Degree Theses. University of Waikato Masters Theses, 2007.
[10] J. Gama, Machine Learning, Kluwer Academic Publishers, pp.219-250, 2004.
[11] R. Quinlan, C 4.5: Programs for Machine Learning, Morgan Kaufmann Publishers. San Mateo, CA, 1993.
[12] R. Kohavi, ?Scaling up the accuracy of Naive Bayes classifiers: a Decision tree hybrid?, In Proc. Of the 2nd International conference on knowledge discovery and data mining, pp. 202-207, 1996.
[13] L.Breiman, ?Random Forests?, Machine Learning, vol.45, pp.5–32, 2001.
[14] H. W. Ian, E. Frank, and M.A. Hall, Data Mining Practical Machine Learning Tools and Techniques, Third Edition. Morgan Kaufmann Publishers, 2012.
[15] M.Tavallaee , E. Bagheri , W. Lu , and A. Ghorbani , ? A detailed analysis of the KDD CUP 99 data set? , Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Security and Defence Applications, pp. 1-6, 2009.

Keywords
Best First Tree, Functional Tree, Naïve Bayes Tree, Particle Swarm Optimization, Heuristic Search.